Human Facial Expression Recognition using Eigen Face and Neural Network
Pushpaja V. Saudagare1, D. S. Chaudhari2
1Pushpaja V. Saudagare, Electronics and Telecommunication Department, Amravati University, GCOE Amravati. India.
2Devendra S. Chaudhari, Electronics and Telecommunication Dept, BE, ME, from Marathwada University, Aurangabad and PhD from Indian Institute of Technology, Bombay, Mumbai, India.
Manuscript received on May 17, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 238-241 | Volume-1 Issue-5, June 2012. | Retrieval Number: E0487061512/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In many face recognition systems the important part is face detection. The task of detecting face is complex due to its variability present across human faces including color, pose, expression, position and orientation. A face detection system based on principal component analysis algorithm and neural network techniques. Facial expression as a natural and efficient way of communication, it can also be considered as a special case of pattern recognition and also many techniques are available. In principal component analysis algorithm, eigenvector and Eigenfaces are identified the initial face image set and these faces are projected onto the Eigenfaces for calculating the weights. These weights created a face database to recognize the face by using neural network. Classification of face detection and token matching can be carried out any neural network for recognizing the facial expression.
Keywords: Eigen face, Eigenvector, face recognition, facial expression recognition and neural network.